Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.

ABSTRACT: Life course epidemiology is hampered by the absence of large studies with exposures and outcomes measured at different life stages in the same individuals. We describe when the effect of an exposure ( A ) on an outcome ( Y ) in a target population is identifiable in a combined ("synthetic") cohort created by pooling an early-life cohort including measures of A with a late-life cohort including measures of Y . We enumerate causal assumptions needed for unbiased effect estimation in the synthetic cohort and illustrate by simulating target populations under four causal models. From each target population, we randomly sampled early- and late-life cohorts and created a synthetic cohort by matching individuals from the two cohorts based on mediators and confounders. We estimated the effect of A

SUBMITTER: Kezios KL 

PROVIDER: S-EPMC11305898 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature